Sub-surface thermal measurement in additive manufacturing via machine learning-enabled high-resolution fiber optic sensing.

Wang, Rongxuan; Wang, Ruixuan; Dou, Chaoran; Yang, Shuo; Gnanasambandam, Raghav; Wang, Anbo; Kong, Zhenyu James

Wang, Rongxuan; Wang, Ruixuan; Dou, Chaoran; Yang, Shuo; Gnanasambandam, Raghav; Wang, Anbo; Kong, Zhenyu James.

Afiliación

Wang R; Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, USA.
Wang R; Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA.
Dou C; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA.
Yang S; Department of Biomedical Engineering, Washington University in Saint Louis, Saint Louis, MO, USA.
Gnanasambandam R; The Department of Industrial and Manufacturing Engineering, Florida A&M University-Florida State University College of Engineering, Tallahassee, FL, USA.
Wang A; Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA.
Kong ZJ; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA. zkong@vt.edu.

Nat Commun ; 15(1): 7568, 2024 Aug 31.

Article en En | MEDLINE | ID: mdl-39217158

ABSTRACT

ABSTRACT

Microstructures of additively manufactured metal parts are crucial since they determine the mechanical properties. The evolution of the microstructures during layer-wise printing is complex due to continuous re-melting and reheating effects. The current approach to studying this phenomenon relies on time-consuming numerical models such as finite element analysis due to the lack of effective sub-surface temperature measurement techniques. Attributed to the miniature footprint, chirped-fiber Bragg grating, a unique type of fiber optical sensor, has great potential to achieve this goal. However, using the traditional demodulation methods, its spatial resolution is limited to the millimeter level. In addition, embedding it during laser additive manufacturing is challenging since the sensor is fragile. This paper implements a machine learning-assisted approach to demodulate the optical signal to thermal distribution and significantly improve spatial resolution to 28.8 µm from the original millimeter level. A sensor embedding technique is also developed to minimize damage to the sensor and part while ensuring close contact. The case study demonstrates the excellent performance of the proposed sensor in measuring sharp thermal gradients and fast cooling rates during the laser powder bed fusion. The developed sensor has a promising potential to study the fundamental physics of metal additive manufacturing processes.

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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